Affinity-based detection is a fundamental method to identify and measure the abundance of biological and biochemical analytes and is one of the most important analytical methods in biotechnology. Affinity-based detectors (or so-called biosensors in case of detecting biological analytes) take advantage of the selective interaction and binding (affinity) of the target analyte with immobilized capturing probes to specifically capture the target analyte onto a solid surface. A goal of a detection platform is to facilitate specific capturing and ultimately to produce a detectable signal based on the captured analytes. The generated signals correlate with the presence of the target analytes in the sample (e.g., toxins, polymers, hormones, DNA strands, proteins, bacteria, etc.), and hence are used to estimate their abundance.
To create target-specific signals in biosensors, the target analytes in the sample volume first need to collide with the capturing layer, interact and bind to the probes, and ultimately take part in a transduction process (i.e., a physiochemical process which produces certain measurable electrical, mechanical, or optical parameters produced solely by the captured entities). The analyte motion in typical biosensor settings (e.g., aqueous biological mediums) is dominated by diffusion spreading, which from a microscopic point of view is a probabilistic mass-transfer process (i.e., random walk events for a single analyte molecule). Accordingly, the analyte collisions with the probes become probabilistic processes. Moreover, because of the quantum-mechanical nature of chemical bond formation, interactions between probes and analytes, are also probabilistic, adding more uncertainty to the capturing procedure. On top of these two processes which can be considered the biochemical noise of the system, there may also be a detector and a readout circuitry (e.g., optical scanners for fluorescent-based transducers), which likely add additional noise to the already noisy signal.
Besides the inevitable uncertainty associated with the target analyte capturing and detecting, in all practical biosensors, binding of other species to the probes (non-specific binding) is also possible. Non-specific binding (e.g., cross-hybridization in DNA microarrays) is generally less probable than the specific binding when target analytes and the interfering species have the same abundance. Nonetheless, when the concentration of the non-specific species becomes much higher than the target analyte, non-specific bindings (or essentially interference) may dominate the measured signal and hence limit the minimum-detectable-level (MDL). In biosensors, the MDL may be either biochemical noise or interference-limited, while the highest detection level (HDL), is solely a function of capturing probe density and its saturation level.
Due to such impediments, as of today, the accuracy of biosensors systems does not satisfy the stringent requirements of many high-performance biotechnology applications in molecular diagnostics and forensics. In addition, biosensors systems have not successfully made the transition to portable and compact point-of-care devices because their detection platforms still consist of fluidic systems and bulky detectors.
One proposed solution to address the challenges of biosensor systems is to use semiconductor fabrication technologies to build compact, high-performance, and cost-efficient biosensor systems. It is envisioned that such systems (i.e., lab-on-a-chip platforms), include not only the fluidic (macro or micro) systems and sample preparation processes, but also the integrated transducers.
The challenge of designing sample preparation modules in biosensors, to some extent, has been addressed in recent years, particularly in the form of micro-fluidic and automated liquid handling systems; however, the integration of the detector and readout circuitry has not been addressed. One reason why the integration of the detector and readout circuitry has not been addressed is the technical challenge of manufacturing transducers using custom surface and bulk MEMS procedures. Another reason is performance and cost justification of monolithic integration of all components.
In recent years, the idea of employing Complementary Metal-oxide-semiconductor (CMOS) fabrication processes, which are the most robust and widely used fabrication processes in the semiconductor industry, for biosensors has emerged. The rationale behind this, as opposed to using MEMS or other custom processes, is the unmatched yield, cost-efficiency, and the integration capabilities of CMOS processes. While CMOS processes, from the electronic design point of view, offer huge degree of design flexibility and system integration, they are not very flexible in terms of form factor, transducer design and interface integration. Challenges remain in designing biosensors to take advantage of the CMOS fabrication method. The primary design challenge using CMOS technology is the interface design between the assay and integrated chip (IC) which requires additional post-fabrication processes for compatibility in detecting targets (e.g., analytes).
Therefore, there is a need in the art for incorporating the use of CMOS fabrication processes in the design of affinity-based biosensor systems.